





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class       lowerCAmelCase__(			_snake_case	,  unittest.TestCase     ):






       '''simple docstring'''


       __snake_case			      =     BlenderbotSmallTokenizer
       __snake_case			      =     False




       def      UpperCamelCase_      (   self				)      ->  Optional[Any]:
            super().setUp()

            _SCREAMING_SNAKE_CASE							:						Dict         =				["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
            _SCREAMING_SNAKE_CASE							:						Tuple         =				dict(zip(__snake_case     ,    range(len(__snake_case				)				)				)				)

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
            _SCREAMING_SNAKE_CASE							:						Dict         =				{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

            _SCREAMING_SNAKE_CASE							:						str         =				os.path.join(self.tmpdirname     ,    VOCAB_FILES_NAMES["vocab_file"]				)
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				os.path.join(self.tmpdirname     ,    VOCAB_FILES_NAMES["merges_file"]				)
            with open(self.vocab_file     ,    "w"     ,    encoding="utf-8"				) as fp:
                 fp.write(json.dumps(__snake_case				) + "\n"				)
            with open(self.merges_file     ,    "w"     ,    encoding="utf-8"				) as fp:
                 fp.write("\n".join(__snake_case				)				)




       def      UpperCamelCase_      (   self     ,    **__lowerCamelCase				)      ->  int:
            kwargs.update(self.special_tokens_map				)
            return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname     ,    **__snake_case				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  Optional[Any]:
            _SCREAMING_SNAKE_CASE							:						Dict         =				"""adapt act apte"""
            _SCREAMING_SNAKE_CASE							:						str         =				"""adapt act apte"""
            return input_text, output_text




       def      UpperCamelCase_      (   self				)      ->  List[Any]:
            _SCREAMING_SNAKE_CASE							:						Tuple         =				BlenderbotSmallTokenizer(self.vocab_file     ,    self.merges_file     ,    **self.special_tokens_map				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				"""adapt act apte"""
            _SCREAMING_SNAKE_CASE							:						str         =				["""adapt""", """act""", """ap@@""", """te"""]
            _SCREAMING_SNAKE_CASE							:						Dict         =				tokenizer.tokenize(__snake_case				)
            self.assertListEqual(__snake_case     ,    __snake_case				)

            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				[0, 1, 2, 3, 4, 5]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case				)     ,    __snake_case				)




       def      UpperCamelCase_      (   self				)      ->  List[str]:
            _SCREAMING_SNAKE_CASE							:						Dict         =				BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"				)
            assert tok("sam"				).input_ids == [1_3_8_4]
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				"""I am a small frog."""
            _SCREAMING_SNAKE_CASE							:						Dict         =				tok([src_text]     ,    padding=__snake_case     ,    truncation=__snake_case				)["""input_ids"""]
            _SCREAMING_SNAKE_CASE							:						Dict         =				tok.batch_decode(__snake_case     ,    skip_special_tokens=__snake_case     ,    clean_up_tokenization_spaces=__snake_case				)[0]
            assert src_text != decoded  # I wish it did!
            assert decoded == "i am a small frog ."







       def      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						Tuple         =				BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"				)
            _SCREAMING_SNAKE_CASE							:						Dict         =				"""I am a small frog ."""
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				"""."""
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				tok(__snake_case				)["""input_ids"""]
            _SCREAMING_SNAKE_CASE							:						int         =				tok(__snake_case				)["""input_ids"""]

            assert encoded[-1] == encoded_dot[0]



'''simple docstring'''


import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_						:  List[Any]				  =						'.'


if __name__ == "__main__":
				UpperCAmelCase_						:  Any				  =						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
				UpperCAmelCase_						:  Dict				  =						[]
				UpperCAmelCase_						:  Union[str, Any]				  =						[]
				with open(doctest_file_path) as fp:
								for line in fp:
												UpperCAmelCase_						:  int				  =						line.strip()
												UpperCAmelCase_						:  str				  =						os.path.join(REPO_PATH, line)
												if not (os.path.isfile(path) or os.path.isdir(path)):
																non_existent_paths.append(line)
												all_paths.append(path)
				if len(non_existent_paths) > 0:
								UpperCAmelCase_						:  int				  =						'\n'.join(non_existent_paths)
								raise ValueError(F"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
				if all_paths != sorted(all_paths):
								raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




